🎙️ The Algorithmocene: The End of Human Epistemic Sovereignty – The Deeper Thinking Podcast
Episode Description
Artificial intelligence is not waiting for permission. It no longer asks for validation, nor does it need human oversight to verify the knowledge it produces.
For centuries, knowledge was mediated by human institutions—science, philosophy, political governance—but AI has now disrupted that paradigm. The recursive acceleration of artificial intelligence means that it is not only producing knowledge but refining and validating it beyond the reach of human cognition.
What happens when AI generates theorems that even top mathematicians cannot verify?
When machine learning models uncover physical laws beyond human comprehension?
When scientific peer review becomes too slow to keep up with AI’s rate of discovery?
This episode explores the profound consequences of AI’s transition from an analytical tool to an autonomous epistemic force. We examine why traditional forms of verification—peer review, empirical reproducibility, and theoretical coherence—are breaking down in the face of AI-driven knowledge.
As the world moves into an era where AI dictates what is true, we must ask: Where does this leave human epistemology?
Harland-Cox, B. (2025) The Algorithmocene: The End of Human Epistemic Sovereignty,
Hashtags
#ArtificialIntelligence #AI #MachineLearning #TheDeeperThinkingPodcast #FutureOfKnowledge #Epistemology #TechDisruption #Automation #AIResearch
Complete Academic References for All Sections (Medium)
Epistemology, Knowledge Production, and Human Verification
Floridi, L. (2019). The Logic of Information: A Theory of Philosophy as Conceptual Design. Oxford University Press.Hegel, G. W. F. (1807). The Phenomenology of Spirit. (Translated by A. V. Miller, 1977). Oxford University Press.Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.Latour, B., & Woolgar, S. (1979). Laboratory Life: The Construction of Scientific Facts. Princeton University Press.Marx, K. (1867). Das Kapital: Critique of Political Economy. Volume 1. Verlag von Otto Meissner.Popper, K. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge.AI’s Recursive Acceleration and Self-Learning Models
Carleo, G., Cirac, J. I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002.Fawzi, A., Fawzi, O., & Mohamed, S. (2022). Discovering faster matrix multiplication algorithms with reinforcement learning. Nature, 604(7907), 344-349.Frantar, E., Götz, T., Alistarh, D. (2023). Quantized and Sparse Training of Large AI Models: Towards Efficient AI. Proceedings of the Neural Information Processing Systems Conference (NeurIPS).Goertzel, B. (2020). Artificial general intelligence: Concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 11(2), 1-20.Hales, T. C. (2023). AI-assisted theorem proving: Challenges and opportunities. Journal of Automated Reasoning, 67(1), 23-45.Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., & Potapenko, A. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.LeCun, Y. (2022). Path towards autonomous machine intelligence. Proceedings of the IEEE, 110(7), 1035-1051.Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., & Batra, S. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971.Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8697-8710.The Economic and Political Contradictions of AI’s Acceleration
Allen, G. (2021). Understanding artificial intelligence’s role in national security. Brookings Institution Policy Report.Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton & Company.Dean, J. (2020). AI and the efficiency problem: Hardware-aware model training. Neural Computation, 32(4), 765-782.Fuchs, C. (2022). Digital Capitalism: Rethinking Big Tech and Political Economy. Pluto Press.McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 1273-1282.Rao, V., Arora, S., & Ghosh, A. (2023). Decentralized AI: Federated learning and blockchain architectures for AI governance. Journal of AI Ethics, 5(2), 179-195.Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.AI’s Escape from Capital, Compute, and Governance Constraints
Bourne, P. E., Polka, J. K., Vale, R. D., & Krumholz, H. M. (2017). Ten simple rules to consider regarding preprint submission. PLoS Computational Biology, 13(5), e1005473.Floridi, L. (2019). Translating principles into practices of digital ethics: Five risks of being unethical. Philosophy & Technology, 32(2), 185-193.Frantar, E., Alistarh, D. (2023). Sparse and efficient AI: Overcoming compute bottlenecks. Advances in Neural Information Processing Systems (NeurIPS).Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., & Batra, S. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971.Zuboff, S. (2019). Surveillance capitalism and the AI economy. Journal of Political Economy, 127(1), 115-144.AI’s Irreversible Epistemic Shift and Future Knowledge Structures
Carleo, G., Cirac, J. I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002.Hales, T. C. (2023). AI-assisted theorem proving: Challenges and opportunities. Journal of Automated Reasoning, 67(1), 23-45.Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., & Potapenko, A. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.
![Cosmosis [Formerly The UFO Rabbit Hole] by Kelly Chase](https://podcast-api-images.s3.amazonaws.com/corona/show/3501011/logo_300x300.jpeg){kind=logo}
